Overview

Dataset statistics

Number of variables32
Number of observations121
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.4 KiB
Average record size in memory257.1 B

Variable types

Numeric12
Categorical20

Alerts

anupam_present has constant value "1"Constant
brand_name has a high cardinality: 121 distinct valuesHigh cardinality
description has a high cardinality: 121 distinct valuesHigh cardinality
episode_number is highly overall correlated with startup_number and 6 other fieldsHigh correlation
startup_number is highly overall correlated with episode_number and 6 other fieldsHigh correlation
startup_ask_amount_lakhs is highly overall correlated with startup_ask_valuationHigh correlation
startup_ask_percentage is highly overall correlated with startup_ask_valuationHigh correlation
startup_ask_valuation is highly overall correlated with startup_ask_amount_lakhs and 1 other fieldsHigh correlation
deal_amount_lakhs is highly overall correlated with deal_equity and 6 other fieldsHigh correlation
deal_equity is highly overall correlated with deal_amount_lakhs and 5 other fieldsHigh correlation
deal_valuation is highly overall correlated with deal_amount_lakhs and 4 other fieldsHigh correlation
loan_amount is highly overall correlated with loan_element_presentHigh correlation
sharks_offering is highly overall correlated with deal_amount_lakhs and 10 other fieldsHigh correlation
amount_per_shark is highly overall correlated with deal_amount_lakhs and 5 other fieldsHigh correlation
equity_per_shark is highly overall correlated with deal_amount_lakhs and 4 other fieldsHigh correlation
deal_offered is highly overall correlated with deal_amount_lakhs and 3 other fieldsHigh correlation
loan_element_present is highly overall correlated with loan_amountHigh correlation
rannvijay_present is highly overall correlated with abish_presentHigh correlation
abish_present is highly overall correlated with rannvijay_presentHigh correlation
aman_present is highly overall correlated with episode_number and 3 other fieldsHigh correlation
aman_invested is highly overall correlated with deal_amount_lakhs and 1 other fieldsHigh correlation
anupam_invested is highly overall correlated with sharks_offeringHigh correlation
ashneer_present is highly overall correlated with episode_number and 3 other fieldsHigh correlation
ashneer_invested is highly overall correlated with sharks_offeringHigh correlation
ghazal_present is highly overall correlated with episode_number and 3 other fieldsHigh correlation
namita_present is highly overall correlated with episode_number and 1 other fieldsHigh correlation
namita_invested is highly overall correlated with sharks_offeringHigh correlation
peyush_present is highly overall correlated with episode_number and 1 other fieldsHigh correlation
peyush_invested is highly overall correlated with sharks_offeringHigh correlation
vineeta_present is highly overall correlated with episode_number and 1 other fieldsHigh correlation
loan_element_present is highly imbalanced (61.8%)Imbalance
rannvijay_present is highly imbalanced (79.0%)Imbalance
abish_present is highly imbalanced (79.0%)Imbalance
ghazal_invested is highly imbalanced (68.1%)Imbalance
namita_present is highly imbalanced (56.1%)Imbalance
startup_number is uniformly distributedUniform
brand_name is uniformly distributedUniform
description is uniformly distributedUniform
startup_number has unique valuesUnique
brand_name has unique valuesUnique
description has unique valuesUnique
deal_amount_lakhs has 54 (44.6%) zerosZeros
deal_equity has 54 (44.6%) zerosZeros
deal_valuation has 55 (45.5%) zerosZeros
loan_amount has 112 (92.6%) zerosZeros
sharks_offering has 54 (44.6%) zerosZeros
amount_per_shark has 54 (44.6%) zerosZeros
equity_per_shark has 54 (44.6%) zerosZeros

Reproduction

Analysis started2023-05-28 06:48:39.731537
Analysis finished2023-05-28 06:48:56.610192
Duration16.88 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

episode_number
Real number (ℝ)

Distinct36
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.305785
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-05-28T12:18:56.702457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median19
Q328
95-th percentile35
Maximum36
Range35
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.375326
Coefficient of variation (CV)0.53742055
Kurtosis-1.1651326
Mean19.305785
Median Absolute Deviation (MAD)9
Skewness-0.094792331
Sum2336
Variance107.64738
MonotonicityIncreasing
2023-05-28T12:18:56.792695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
19 4
 
3.3%
16 4
 
3.3%
34 4
 
3.3%
33 4
 
3.3%
32 4
 
3.3%
31 4
 
3.3%
30 4
 
3.3%
27 4
 
3.3%
23 4
 
3.3%
22 4
 
3.3%
Other values (26) 81
66.9%
ValueCountFrequency (%)
1 3
2.5%
2 3
2.5%
3 3
2.5%
4 3
2.5%
5 3
2.5%
6 3
2.5%
7 3
2.5%
8 3
2.5%
9 3
2.5%
10 3
2.5%
ValueCountFrequency (%)
36 4
3.3%
35 3
2.5%
34 4
3.3%
33 4
3.3%
32 4
3.3%
31 4
3.3%
30 4
3.3%
29 3
2.5%
28 3
2.5%
27 4
3.3%

startup_number
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct121
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61
Minimum1
Maximum121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-05-28T12:18:56.886945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q131
median61
Q391
95-th percentile115
Maximum121
Range120
Interquartile range (IQR)60

Descriptive statistics

Standard deviation35.073732
Coefficient of variation (CV)0.57497921
Kurtosis-1.2
Mean61
Median Absolute Deviation (MAD)30
Skewness0
Sum7381
Variance1230.1667
MonotonicityStrictly increasing
2023-05-28T12:18:57.004915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
62 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
86 1
 
0.8%
85 1
 
0.8%
84 1
 
0.8%
83 1
 
0.8%
Other values (111) 111
91.7%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
121 1
0.8%
120 1
0.8%
119 1
0.8%
118 1
0.8%
117 1
0.8%
116 1
0.8%
115 1
0.8%
114 1
0.8%
113 1
0.8%
112 1
0.8%

brand_name
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct121
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
BluePine Industries
 
1
The State Plate
 
1
Kunafa World
 
1
Humpy A2
 
1
Insurance Samadhan
 
1
Other values (116)
116 

Length

Max length30
Median length19
Mean length11.669421
Min length3

Characters and Unicode

Total characters1412
Distinct characters59
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)100.0%

Sample

1st rowBluePine Industries
2nd rowBooz scooters
3rd rowHeart up my Sleeves
4th rowTagz Foods
5th rowHead and Heart

Common Values

ValueCountFrequency (%)
BluePine Industries 1
 
0.8%
The State Plate 1
 
0.8%
Kunafa World 1
 
0.8%
Humpy A2 1
 
0.8%
Insurance Samadhan 1
 
0.8%
Aliste Technologies 1
 
0.8%
Watt Technovations 1
 
0.8%
Theka Coffee 1
 
0.8%
Rare Planet 1
 
0.8%
Julaa Automation 1
 
0.8%
Other values (111) 111
91.7%

Length

2023-05-28T12:18:57.433668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 5
 
2.2%
foods 4
 
1.7%
india 4
 
1.7%
and 3
 
1.3%
heart 2
 
0.9%
technologies 2
 
0.9%
project 2
 
0.9%
labs 2
 
0.9%
kitchen 2
 
0.9%
beyond 2
 
0.9%
Other values (198) 203
87.9%

Most occurring characters

ValueCountFrequency (%)
e 121
 
8.6%
a 120
 
8.5%
110
 
7.8%
o 98
 
6.9%
i 81
 
5.7%
n 80
 
5.7%
r 73
 
5.2%
s 58
 
4.1%
t 55
 
3.9%
d 48
 
3.4%
Other values (49) 568
40.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1034
73.2%
Uppercase Letter 255
 
18.1%
Space Separator 110
 
7.8%
Decimal Number 6
 
0.4%
Other Punctuation 5
 
0.4%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 121
11.7%
a 120
11.6%
o 98
 
9.5%
i 81
 
7.8%
n 80
 
7.7%
r 73
 
7.1%
s 58
 
5.6%
t 55
 
5.3%
d 48
 
4.6%
l 39
 
3.8%
Other values (16) 261
25.2%
Uppercase Letter
ValueCountFrequency (%)
S 26
 
10.2%
A 21
 
8.2%
T 21
 
8.2%
M 17
 
6.7%
P 16
 
6.3%
F 14
 
5.5%
B 14
 
5.5%
C 13
 
5.1%
I 12
 
4.7%
H 11
 
4.3%
Other values (14) 90
35.3%
Decimal Number
ValueCountFrequency (%)
2 2
33.3%
7 1
16.7%
0 1
16.7%
5 1
16.7%
6 1
16.7%
Other Punctuation
ValueCountFrequency (%)
' 4
80.0%
. 1
 
20.0%
Space Separator
ValueCountFrequency (%)
110
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1289
91.3%
Common 123
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 121
 
9.4%
a 120
 
9.3%
o 98
 
7.6%
i 81
 
6.3%
n 80
 
6.2%
r 73
 
5.7%
s 58
 
4.5%
t 55
 
4.3%
d 48
 
3.7%
l 39
 
3.0%
Other values (40) 516
40.0%
Common
ValueCountFrequency (%)
110
89.4%
' 4
 
3.3%
2 2
 
1.6%
- 2
 
1.6%
. 1
 
0.8%
7 1
 
0.8%
0 1
 
0.8%
5 1
 
0.8%
6 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 121
 
8.6%
a 120
 
8.5%
110
 
7.8%
o 98
 
6.9%
i 81
 
5.7%
n 80
 
5.7%
r 73
 
5.2%
s 58
 
4.1%
t 55
 
3.9%
d 48
 
3.4%
Other values (49) 568
40.2%

description
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct121
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Frozen Momos
 
1
Delicacies
 
1
Kunafa
 
1
Organic Milk Products
 
1
Insurance Solutions
 
1
Other values (116)
116 

Length

Max length48
Median length32
Mean length18.421488
Min length2

Characters and Unicode

Total characters2229
Distinct characters53
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)100.0%

Sample

1st rowFrozen Momos
2nd rowRenting e-bike for mobility in private spaces
3rd rowDetachable Sleeves
4th rowHealthy Potato Chips
5th rowBrain Development Course

Common Values

ValueCountFrequency (%)
Frozen Momos 1
 
0.8%
Delicacies 1
 
0.8%
Kunafa 1
 
0.8%
Organic Milk Products 1
 
0.8%
Insurance Solutions 1
 
0.8%
Automation Solutions 1
 
0.8%
Ventilated PPE Kits 1
 
0.8%
Coffee Products 1
 
0.8%
Handicrafts 1
 
0.8%
Automatic Cradle 1
 
0.8%
Other values (111) 111
91.7%

Length

2023-05-28T12:18:57.535263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
products 9
 
3.0%
app 8
 
2.6%
solutions 6
 
2.0%
food 5
 
1.7%
healthy 4
 
1.3%
for 4
 
1.3%
and 4
 
1.3%
smart 4
 
1.3%
device 3
 
1.0%
ayurvedic 3
 
1.0%
Other values (231) 253
83.5%

Most occurring characters

ValueCountFrequency (%)
e 223
 
10.0%
182
 
8.2%
a 152
 
6.8%
t 146
 
6.6%
o 141
 
6.3%
i 137
 
6.1%
r 130
 
5.8%
s 129
 
5.8%
n 115
 
5.2%
l 98
 
4.4%
Other values (43) 776
34.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1758
78.9%
Uppercase Letter 275
 
12.3%
Space Separator 182
 
8.2%
Dash Punctuation 11
 
0.5%
Decimal Number 2
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 223
12.7%
a 152
 
8.6%
t 146
 
8.3%
o 141
 
8.0%
i 137
 
7.8%
r 130
 
7.4%
s 129
 
7.3%
n 115
 
6.5%
l 98
 
5.6%
c 74
 
4.2%
Other values (15) 413
23.5%
Uppercase Letter
ValueCountFrequency (%)
S 35
12.7%
P 32
11.6%
A 25
 
9.1%
C 22
 
8.0%
F 20
 
7.3%
D 18
 
6.5%
E 14
 
5.1%
I 13
 
4.7%
B 13
 
4.7%
H 13
 
4.7%
Other values (13) 70
25.5%
Decimal Number
ValueCountFrequency (%)
8 1
50.0%
0 1
50.0%
Space Separator
ValueCountFrequency (%)
182
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2033
91.2%
Common 196
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 223
 
11.0%
a 152
 
7.5%
t 146
 
7.2%
o 141
 
6.9%
i 137
 
6.7%
r 130
 
6.4%
s 129
 
6.3%
n 115
 
5.7%
l 98
 
4.8%
c 74
 
3.6%
Other values (38) 688
33.8%
Common
ValueCountFrequency (%)
182
92.9%
- 11
 
5.6%
& 1
 
0.5%
8 1
 
0.5%
0 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 223
 
10.0%
182
 
8.2%
a 152
 
6.8%
t 146
 
6.6%
o 141
 
6.3%
i 137
 
6.1%
r 130
 
5.8%
s 129
 
5.8%
n 115
 
5.2%
l 98
 
4.4%
Other values (43) 776
34.8%

deal_offered
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
68 
0
53 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 68
56.2%
0 53
43.8%

Length

2023-05-28T12:18:57.629487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:18:57.740530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1 68
56.2%
0 53
43.8%

Most occurring characters

ValueCountFrequency (%)
1 68
56.2%
0 53
43.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 68
56.2%
0 53
43.8%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 68
56.2%
0 53
43.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 68
56.2%
0 53
43.8%

startup_ask_amount_lakhs
Real number (ℝ)

Distinct26
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean312.33885
Minimum0.00101
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-05-28T12:18:57.809636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.00101
5-th percentile25
Q145
median50
Q380
95-th percentile125
Maximum30000
Range29999.999
Interquartile range (IQR)35

Descriptive statistics

Standard deviation2721.6405
Coefficient of variation (CV)8.713743
Kurtosis120.9505
Mean312.33885
Median Absolute Deviation (MAD)20
Skewness10.99666
Sum37793.001
Variance7407326.9
MonotonicityNot monotonic
2023-05-28T12:18:57.900193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
50 32
26.4%
100 18
14.9%
75 14
11.6%
30 9
 
7.4%
40 9
 
7.4%
80 5
 
4.1%
60 4
 
3.3%
35 4
 
3.3%
150 3
 
2.5%
45 3
 
2.5%
Other values (16) 20
16.5%
ValueCountFrequency (%)
0.00101 1
 
0.8%
5 1
 
0.8%
10 1
 
0.8%
15 1
 
0.8%
20 2
 
1.7%
25 2
 
1.7%
30 9
7.4%
35 4
3.3%
40 9
7.4%
45 3
 
2.5%
ValueCountFrequency (%)
30000 1
 
0.8%
300 1
 
0.8%
200 1
 
0.8%
150 3
 
2.5%
125 1
 
0.8%
120 1
 
0.8%
100 18
14.9%
90 2
 
1.7%
80 5
 
4.1%
75 14
11.6%

startup_ask_percentage
Real number (ℝ)

Distinct19
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0833058
Minimum0.25
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-05-28T12:18:57.976296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile1
Q12
median5
Q37
95-th percentile10
Maximum25
Range24.75
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.8825536
Coefficient of variation (CV)0.76378517
Kurtosis6.2950925
Mean5.0833058
Median Absolute Deviation (MAD)2.5
Skewness1.9426984
Sum615.08
Variance15.074222
MonotonicityNot monotonic
2023-05-28T12:18:58.063981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
5 30
24.8%
10 16
13.2%
1 14
11.6%
2 14
11.6%
4 11
 
9.1%
3 9
 
7.4%
8 5
 
4.1%
2.5 5
 
4.1%
7.5 5
 
4.1%
15 2
 
1.7%
Other values (9) 10
 
8.3%
ValueCountFrequency (%)
0.25 1
 
0.8%
0.33 1
 
0.8%
1 14
11.6%
1.25 1
 
0.8%
1.75 1
 
0.8%
2 14
11.6%
2.5 5
 
4.1%
3 9
7.4%
3.5 1
 
0.8%
4 11
9.1%
ValueCountFrequency (%)
25 1
 
0.8%
20 1
 
0.8%
15 2
 
1.7%
10 16
13.2%
8 5
 
4.1%
7.5 5
 
4.1%
7 2
 
1.7%
6 1
 
0.8%
5 30
24.8%
4 11
 
9.1%

startup_ask_valuation
Real number (ℝ)

Distinct50
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4230.1827
Minimum0.01
Maximum120000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-05-28T12:18:58.165103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile300
Q1666.67
median1333.33
Q33000
95-th percentile10000
Maximum120000
Range119999.99
Interquartile range (IQR)2333.33

Descriptive statistics

Standard deviation12329.895
Coefficient of variation (CV)2.9147428
Kurtosis66.855418
Mean4230.1827
Median Absolute Deviation (MAD)833.33
Skewness7.5982775
Sum511852.11
Variance1.520263 × 108
MonotonicityNot monotonic
2023-05-28T12:18:58.279155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 11
 
9.1%
500 11
 
9.1%
10000 8
 
6.6%
2000 7
 
5.8%
2500 6
 
5.0%
1250 5
 
4.1%
1875 5
 
4.1%
1500 5
 
4.1%
5000 5
 
4.1%
400 4
 
3.3%
Other values (40) 54
44.6%
ValueCountFrequency (%)
0.01 1
 
0.8%
50 1
 
0.8%
100 1
 
0.8%
200 1
 
0.8%
250 1
 
0.8%
266.67 1
 
0.8%
300 3
 
2.5%
400 4
 
3.3%
470 1
 
0.8%
500 11
9.1%
ValueCountFrequency (%)
120000 1
 
0.8%
45000 1
 
0.8%
40000 1
 
0.8%
30000 1
 
0.8%
10000 8
6.6%
8000 1
 
0.8%
7500 2
 
1.7%
7000 1
 
0.8%
6500 1
 
0.8%
5714 1
 
0.8%

deal_amount_lakhs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.925629
Minimum0
Maximum150
Zeros54
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-05-28T12:18:58.364785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median21
Q350
95-th percentile100
Maximum150
Range150
Interquartile range (IQR)50

Descriptive statistics

Standard deviation36.847011
Coefficient of variation (CV)1.1541515
Kurtosis-0.30997096
Mean31.925629
Median Absolute Deviation (MAD)21
Skewness0.84552667
Sum3863.0011
Variance1357.7022
MonotonicityNot monotonic
2023-05-28T12:18:58.453108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 54
44.6%
50 15
 
12.4%
100 12
 
9.9%
75 7
 
5.8%
40 6
 
5.0%
25 4
 
3.3%
30 4
 
3.3%
60 3
 
2.5%
80 2
 
1.7%
10 2
 
1.7%
Other values (12) 12
 
9.9%
ValueCountFrequency (%)
0 54
44.6%
5 × 10-51
 
0.8%
0.00101 1
 
0.8%
1 1
 
0.8%
10 2
 
1.7%
20 1
 
0.8%
21 1
 
0.8%
25 4
 
3.3%
30 4
 
3.3%
35 1
 
0.8%
ValueCountFrequency (%)
150 1
 
0.8%
105 1
 
0.8%
100 12
9.9%
80 2
 
1.7%
75 7
5.8%
70 1
 
0.8%
65 1
 
0.8%
60 3
 
2.5%
56 1
 
0.8%
50 15
12.4%

deal_equity
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7994215
Minimum0
Maximum75
Zeros54
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-05-28T12:18:58.531733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q315
95-th percentile33.3
Maximum75
Range75
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.948175
Coefficient of variation (CV)1.4714802
Kurtosis6.0176742
Mean8.7994215
Median Absolute Deviation (MAD)3
Skewness2.1467433
Sum1064.73
Variance167.65522
MonotonicityNot monotonic
2023-05-28T12:18:58.625959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 54
44.6%
15 9
 
7.4%
20 7
 
5.8%
10 7
 
5.8%
6 5
 
4.1%
4 5
 
4.1%
25 4
 
3.3%
3 4
 
3.3%
30 3
 
2.5%
50 2
 
1.7%
Other values (18) 21
 
17.4%
ValueCountFrequency (%)
0 54
44.6%
1 1
 
0.8%
1.5 1
 
0.8%
2 1
 
0.8%
2.5 1
 
0.8%
2.68 1
 
0.8%
2.75 1
 
0.8%
3 4
 
3.3%
3.5 1
 
0.8%
4 5
 
4.1%
ValueCountFrequency (%)
75 1
 
0.8%
50 2
 
1.7%
40 2
 
1.7%
35 1
 
0.8%
33.3 1
 
0.8%
30 3
2.5%
25 4
3.3%
24 2
 
1.7%
21 1
 
0.8%
20 7
5.8%

deal_valuation
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)33.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean473.77083
Minimum0
Maximum6666.67
Zeros55
Zeros (%)45.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-05-28T12:18:58.720209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median100
Q3500
95-th percentile2500
Maximum6666.67
Range6666.67
Interquartile range (IQR)500

Descriptive statistics

Standard deviation925.69347
Coefficient of variation (CV)1.9538845
Kurtosis17.139564
Mean473.77083
Median Absolute Deviation (MAD)100
Skewness3.5170249
Sum57326.27
Variance856908.4
MonotonicityNot monotonic
2023-05-28T12:18:58.820342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 55
45.5%
500 5
 
4.1%
1000 5
 
4.1%
100 4
 
3.3%
2500 4
 
3.3%
200 3
 
2.5%
666.67 3
 
2.5%
250 3
 
2.5%
166.67 2
 
1.7%
1333.33 2
 
1.7%
Other values (30) 35
28.9%
ValueCountFrequency (%)
0 55
45.5%
0.03 1
 
0.8%
25 1
 
0.8%
33.33 1
 
0.8%
80 1
 
0.8%
83.33 1
 
0.8%
100 4
 
3.3%
125 1
 
0.8%
133.33 2
 
1.7%
142.86 1
 
0.8%
ValueCountFrequency (%)
6666.67 1
 
0.8%
3500 1
 
0.8%
2798.51 1
 
0.8%
2545.45 1
 
0.8%
2500 4
3.3%
2166.67 1
 
0.8%
2000 1
 
0.8%
1666.67 2
1.7%
1500 1
 
0.8%
1428.57 1
 
0.8%

loan_element_present
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
112 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 112
92.6%
1 9
 
7.4%

Length

2023-05-28T12:18:58.914623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:18:58.993370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 112
92.6%
1 9
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 112
92.6%
1 9
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 112
92.6%
1 9
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 112
92.6%
1 9
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 112
92.6%
1 9
 
7.4%

loan_amount
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9008264
Minimum0
Maximum99
Zeros112
Zeros (%)92.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-05-28T12:18:59.055870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25
Maximum99
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.150724
Coefficient of variation (CV)4.188711
Kurtosis35.872499
Mean2.9008264
Median Absolute Deviation (MAD)0
Skewness5.5124559
Sum351
Variance147.64008
MonotonicityNot monotonic
2023-05-28T12:18:59.118870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 112
92.6%
30 2
 
1.7%
25 2
 
1.7%
50 2
 
1.7%
99 1
 
0.8%
22 1
 
0.8%
20 1
 
0.8%
ValueCountFrequency (%)
0 112
92.6%
20 1
 
0.8%
22 1
 
0.8%
25 2
 
1.7%
30 2
 
1.7%
50 2
 
1.7%
99 1
 
0.8%
ValueCountFrequency (%)
99 1
 
0.8%
50 2
 
1.7%
30 2
 
1.7%
25 2
 
1.7%
22 1
 
0.8%
20 1
 
0.8%
0 112
92.6%

rannvijay_present
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
117 
0
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 117
96.7%
0 4
 
3.3%

Length

2023-05-28T12:18:59.197488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:18:59.276067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1 117
96.7%
0 4
 
3.3%

Most occurring characters

ValueCountFrequency (%)
1 117
96.7%
0 4
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 117
96.7%
0 4
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 117
96.7%
0 4
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 117
96.7%
0 4
 
3.3%

abish_present
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
117 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 117
96.7%
1 4
 
3.3%

Length

2023-05-28T12:18:59.361339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:18:59.439464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 117
96.7%
1 4
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 117
96.7%
1 4
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 117
96.7%
1 4
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 117
96.7%
1 4
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 117
96.7%
1 4
 
3.3%

aman_present
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
102 
0
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 102
84.3%
0 19
 
15.7%

Length

2023-05-28T12:18:59.502456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:18:59.581037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1 102
84.3%
0 19
 
15.7%

Most occurring characters

ValueCountFrequency (%)
1 102
84.3%
0 19
 
15.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 102
84.3%
0 19
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 102
84.3%
0 19
 
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 102
84.3%
0 19
 
15.7%

aman_invested
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
92 
1
29 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 92
76.0%
1 29
 
24.0%

Length

2023-05-28T12:18:59.659206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:18:59.737833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 92
76.0%
1 29
 
24.0%

Most occurring characters

ValueCountFrequency (%)
0 92
76.0%
1 29
 
24.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92
76.0%
1 29
 
24.0%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92
76.0%
1 29
 
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92
76.0%
1 29
 
24.0%

anupam_present
Categorical

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
121 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 121
100.0%

Length

2023-05-28T12:18:59.800806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:18:59.890975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1 121
100.0%

Most occurring characters

ValueCountFrequency (%)
1 121
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 121
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 121
100.0%

anupam_invested
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
97 
1
24 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 97
80.2%
1 24
 
19.8%

Length

2023-05-28T12:18:59.949550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:19:00.028166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 97
80.2%
1 24
 
19.8%

Most occurring characters

ValueCountFrequency (%)
0 97
80.2%
1 24
 
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97
80.2%
1 24
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97
80.2%
1 24
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97
80.2%
1 24
 
19.8%

ashneer_present
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
98 
0
23 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 98
81.0%
0 23
 
19.0%

Length

2023-05-28T12:19:00.107501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:19:00.185907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1 98
81.0%
0 23
 
19.0%

Most occurring characters

ValueCountFrequency (%)
1 98
81.0%
0 23
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 98
81.0%
0 23
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 98
81.0%
0 23
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 98
81.0%
0 23
 
19.0%

ashneer_invested
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
100 
1
21 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 100
82.6%
1 21
 
17.4%

Length

2023-05-28T12:19:00.257405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:19:00.327436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 100
82.6%
1 21
 
17.4%

Most occurring characters

ValueCountFrequency (%)
0 100
82.6%
1 21
 
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 100
82.6%
1 21
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 100
82.6%
1 21
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 100
82.6%
1 21
 
17.4%

ghazal_present
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
95 
1
26 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 95
78.5%
1 26
 
21.5%

Length

2023-05-28T12:19:00.410102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:19:00.493396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 95
78.5%
1 26
 
21.5%

Most occurring characters

ValueCountFrequency (%)
0 95
78.5%
1 26
 
21.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 95
78.5%
1 26
 
21.5%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 95
78.5%
1 26
 
21.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 95
78.5%
1 26
 
21.5%

ghazal_invested
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
114 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 114
94.2%
1 7
 
5.8%

Length

2023-05-28T12:19:00.567478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:19:00.630478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 114
94.2%
1 7
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 114
94.2%
1 7
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 114
94.2%
1 7
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 114
94.2%
1 7
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 114
94.2%
1 7
 
5.8%

namita_present
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
110 
0
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 110
90.9%
0 11
 
9.1%

Length

2023-05-28T12:19:00.709201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:19:00.787325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1 110
90.9%
0 11
 
9.1%

Most occurring characters

ValueCountFrequency (%)
1 110
90.9%
0 11
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 110
90.9%
0 11
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 110
90.9%
0 11
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 110
90.9%
0 11
 
9.1%

namita_invested
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
97 
1
24 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 97
80.2%
1 24
 
19.8%

Length

2023-05-28T12:19:00.865450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:19:00.947177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 97
80.2%
1 24
 
19.8%

Most occurring characters

ValueCountFrequency (%)
0 97
80.2%
1 24
 
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97
80.2%
1 24
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97
80.2%
1 24
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97
80.2%
1 24
 
19.8%

peyush_present
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
92 
0
29 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 92
76.0%
0 29
 
24.0%

Length

2023-05-28T12:19:01.014311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:19:01.092913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1 92
76.0%
0 29
 
24.0%

Most occurring characters

ValueCountFrequency (%)
1 92
76.0%
0 29
 
24.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 92
76.0%
0 29
 
24.0%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 92
76.0%
0 29
 
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 92
76.0%
0 29
 
24.0%

peyush_invested
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
93 
1
28 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 93
76.9%
1 28
 
23.1%

Length

2023-05-28T12:19:01.155413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:19:01.249675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 93
76.9%
1 28
 
23.1%

Most occurring characters

ValueCountFrequency (%)
0 93
76.9%
1 28
 
23.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 93
76.9%
1 28
 
23.1%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 93
76.9%
1 28
 
23.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 93
76.9%
1 28
 
23.1%

vineeta_present
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
70 
0
51 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 70
57.9%
0 51
42.1%

Length

2023-05-28T12:19:01.312675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:19:01.391274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1 70
57.9%
0 51
42.1%

Most occurring characters

ValueCountFrequency (%)
1 70
57.9%
0 51
42.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 70
57.9%
0 51
42.1%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 70
57.9%
0 51
42.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 70
57.9%
0 51
42.1%

vineeta_invested
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
105 
1
16 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 105
86.8%
1 16
 
13.2%

Length

2023-05-28T12:19:01.472256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T12:19:01.547149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 105
86.8%
1 16
 
13.2%

Most occurring characters

ValueCountFrequency (%)
0 105
86.8%
1 16
 
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 105
86.8%
1 16
 
13.2%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 105
86.8%
1 16
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 105
86.8%
1 16
 
13.2%

sharks_offering
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.231405
Minimum0
Maximum5
Zeros54
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-05-28T12:19:01.610149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4068898
Coefficient of variation (CV)1.1425078
Kurtosis0.0069148469
Mean1.231405
Median Absolute Deviation (MAD)1
Skewness0.94817545
Sum149
Variance1.9793388
MonotonicityNot monotonic
2023-05-28T12:19:01.673700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 54
44.6%
1 22
18.2%
2 21
 
17.4%
3 15
 
12.4%
4 5
 
4.1%
5 4
 
3.3%
ValueCountFrequency (%)
0 54
44.6%
1 22
18.2%
2 21
 
17.4%
3 15
 
12.4%
4 5
 
4.1%
5 4
 
3.3%
ValueCountFrequency (%)
5 4
 
3.3%
4 5
 
4.1%
3 15
 
12.4%
2 21
 
17.4%
1 22
18.2%
0 54
44.6%

amount_per_shark
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.004134
Minimum0
Maximum100
Zeros54
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-05-28T12:19:01.767061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q325
95-th percentile70
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation23.51249
Coefficient of variation (CV)1.3059495
Kurtosis1.7346314
Mean18.004134
Median Absolute Deviation (MAD)10
Skewness1.4518815
Sum2178.5003
Variance552.83717
MonotonicityNot monotonic
2023-05-28T12:19:01.845663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 54
44.6%
25 12
 
9.9%
50 11
 
9.1%
20 7
 
5.8%
10 6
 
5.0%
40 3
 
2.5%
75 3
 
2.5%
100 2
 
1.7%
37.5 2
 
1.7%
33.33333333 2
 
1.7%
Other values (18) 19
 
15.7%
ValueCountFrequency (%)
0 54
44.6%
1.67 × 10-51
 
0.8%
0.0002525 1
 
0.8%
1 1
 
0.8%
5 1
 
0.8%
7 1
 
0.8%
8.333333333 1
 
0.8%
10 6
 
5.0%
12.5 1
 
0.8%
13.33333333 1
 
0.8%
ValueCountFrequency (%)
100 2
 
1.7%
80 1
 
0.8%
75 3
 
2.5%
70 1
 
0.8%
65 1
 
0.8%
50 11
9.1%
40 3
 
2.5%
37.5 2
 
1.7%
35 1
 
0.8%
33.33333333 2
 
1.7%

equity_per_shark
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.448595
Minimum0
Maximum75
Zeros54
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-05-28T12:19:01.949798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.25
Q36
95-th percentile25
Maximum75
Range75
Interquartile range (IQR)6

Descriptive statistics

Standard deviation10.651783
Coefficient of variation (CV)1.9549596
Kurtosis17.921125
Mean5.448595
Median Absolute Deviation (MAD)1.25
Skewness3.7484669
Sum659.28
Variance113.46048
MonotonicityNot monotonic
2023-05-28T12:19:02.039834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 54
44.6%
5 7
 
5.8%
3 5
 
4.1%
15 5
 
4.1%
1 4
 
3.3%
10 4
 
3.3%
2 4
 
3.3%
6 3
 
2.5%
25 3
 
2.5%
4 2
 
1.7%
Other values (25) 30
24.8%
ValueCountFrequency (%)
0 54
44.6%
0.75 1
 
0.8%
1 4
 
3.3%
1.2 1
 
0.8%
1.25 1
 
0.8%
1.333333333 1
 
0.8%
1.666666667 1
 
0.8%
1.75 1
 
0.8%
2 4
 
3.3%
2.5 2
 
1.7%
ValueCountFrequency (%)
75 1
 
0.8%
50 1
 
0.8%
40 2
 
1.7%
25 3
2.5%
20 2
 
1.7%
17.5 1
 
0.8%
16.65 1
 
0.8%
15 5
4.1%
12.5 1
 
0.8%
12 1
 
0.8%

Interactions

2023-05-28T12:18:54.697038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:42.709497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:43.778962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:44.817103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:46.090757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:47.086464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:48.159826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:49.229240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:50.301752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:51.676144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:52.684206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:53.679933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:54.795565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:42.835326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:43.867750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:44.908167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:46.180340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:47.187599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:48.259685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:49.310434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:50.657296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:51.764025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:52.768282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:53.771732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:54.893697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:42.923894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:43.956025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:44.991526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:46.265687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:47.286178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:48.340939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:49.414668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:50.752791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:51.844043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:52.851614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:53.861121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:54.981008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:43.005655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:44.046644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:45.080971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:46.348480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:47.361616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:48.417527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:49.510749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:50.853528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:51.929110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:52.938815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:53.943312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:55.067750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:43.093211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:44.124358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:45.165573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:46.431247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:47.462777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:48.502951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:49.597157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:50.943259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:52.006824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:53.017008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:54.024920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:55.156900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:43.180633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:44.212861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:45.474035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:46.510053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:47.548448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:48.606303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:49.689914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:51.032261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:52.094944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:53.100767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:54.108032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:55.248780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:43.268596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:44.302669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:45.564222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:46.599814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:47.639458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:48.697843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:49.780069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:51.124563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:52.183816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:53.191019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:54.191604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:55.327238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:43.355887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:44.391215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:45.656785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:46.679143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:47.729229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:48.788403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:49.871202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:51.228805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:52.271761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:53.283178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:54.286765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:55.429247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:43.443915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:44.476158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:45.745939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:46.763275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:47.808813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:48.877110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:49.964912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:51.313914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:52.356339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:53.362156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:54.374351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:55.526131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:43.526591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:44.563933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:45.820052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:46.855915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:47.885580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:48.966327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:50.046246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:51.409680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:52.436716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:53.431835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:54.457531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:55.615133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:43.596421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:44.642467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:45.915615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:46.918893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:47.998433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:49.053001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:50.136232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:51.501906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:52.519669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:53.524776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:54.531798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:55.700289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:43.688448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:44.734044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:46.001363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:47.010056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:48.080523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:49.139122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:50.215331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:51.586654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:52.601417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:53.602684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-28T12:18:54.619414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-05-28T12:19:02.172406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
episode_numberstartup_numberstartup_ask_amount_lakhsstartup_ask_percentagestartup_ask_valuationdeal_amount_lakhsdeal_equitydeal_valuationloan_amountsharks_offeringamount_per_sharkequity_per_sharkdeal_offeredloan_element_presentrannvijay_presentabish_presentaman_presentaman_investedanupam_investedashneer_presentashneer_investedghazal_presentghazal_investednamita_presentnamita_investedpeyush_presentpeyush_investedvineeta_presentvineeta_invested
episode_number1.0001.0000.118-0.0940.105-0.199-0.196-0.1910.005-0.162-0.212-0.2160.0980.0000.4100.4100.8840.2750.0480.8030.2250.7940.3490.5810.0000.8410.2480.7380.237
startup_number1.0001.0000.117-0.1000.109-0.206-0.202-0.1970.004-0.168-0.218-0.2210.0820.1190.4870.4870.8280.2800.0620.7590.1550.7710.3610.7330.0000.7580.2060.7960.313
startup_ask_amount_lakhs0.1180.1171.000-0.4360.781-0.019-0.329-0.0150.079-0.272-0.031-0.2910.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
startup_ask_percentage-0.094-0.100-0.4361.000-0.862-0.1210.218-0.225-0.0740.025-0.1070.1720.1430.0000.0000.0000.1220.0000.0000.0000.0960.1190.0000.0000.0000.2490.0000.1440.000
startup_ask_valuation0.1050.1090.781-0.8621.0000.057-0.3160.1390.083-0.1590.042-0.2650.1380.0000.3010.3010.0000.0000.0000.0000.0000.0000.0000.0000.0000.1020.0000.0680.000
deal_amount_lakhs-0.199-0.206-0.019-0.1210.0571.0000.7700.9510.1070.8470.9350.7640.8760.1650.0000.0000.0000.5100.4920.0000.3570.0000.0000.0000.4270.1140.4820.0570.414
deal_equity-0.196-0.202-0.3290.218-0.3160.7701.0000.6730.2050.8270.7770.9760.6120.3270.0000.0000.0000.3130.4530.0000.2540.0000.2390.1950.2650.0000.3550.0000.450
deal_valuation-0.191-0.197-0.015-0.2250.1390.9510.6731.0000.1720.8070.9210.6910.4060.0000.0000.0000.0000.3770.2820.0000.3410.0000.0000.0000.2830.1640.2110.0000.000
loan_amount0.0050.0040.079-0.0740.0830.1070.2050.1721.0000.0800.2130.2570.1720.9870.2880.2880.0000.0000.0000.0000.1700.0000.0000.1190.0000.0000.3080.0000.000
sharks_offering-0.162-0.168-0.2720.025-0.1590.8470.8270.8070.0801.0000.7120.7450.9660.4120.0000.0000.0000.5390.5920.0000.5430.1610.3950.0000.5470.0620.5630.0000.446
amount_per_shark-0.212-0.218-0.031-0.1070.0420.9350.7770.9210.2130.7121.0000.8360.7720.0930.0000.0000.0000.2810.4360.0000.2760.0000.0000.0000.3380.0000.2960.0000.443
equity_per_shark-0.216-0.221-0.2910.172-0.2650.7640.9760.6910.2570.7450.8361.0000.3660.3880.0000.0000.1370.0000.2950.0680.0000.0000.0000.2210.0000.0000.1830.0000.213
deal_offered0.0980.0820.0000.1430.1380.8760.6120.4060.1720.9660.7720.3661.0000.1990.0000.0000.0000.4690.4100.0000.3730.0000.1590.0000.4100.0000.4580.0920.308
loan_element_present0.0000.1190.0000.0000.0000.1650.3270.0000.9870.4120.0930.3880.1991.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1560.0000.000
rannvijay_present0.4100.4870.0000.0000.3010.0000.0000.0000.2880.0000.0000.0000.0000.0001.0000.8700.0000.0000.0000.3110.0000.0000.0000.0000.0000.0000.0000.0630.000
abish_present0.4100.4870.0000.0000.3010.0000.0000.0000.2880.0000.0000.0000.0000.0000.8701.0000.0000.0000.0000.3110.0000.0000.0000.0000.0000.0000.0000.0630.000
aman_present0.8840.8280.0000.1220.0000.0000.0000.0000.0000.0000.0000.1370.0000.0000.0000.0001.0000.1960.0000.8610.1410.7950.4200.0330.0000.1960.0670.3350.000
aman_invested0.2750.2800.0000.0000.0000.5100.3130.3770.0000.5390.2810.0000.4690.0000.0000.0000.1961.0000.1580.1760.2650.2040.0350.0000.2650.0000.0000.1860.000
anupam_invested0.0480.0620.0000.0000.0000.4920.4530.2820.0000.5920.4360.2950.4100.0000.0000.0000.0000.1581.0000.0000.1590.0000.0380.0000.0000.0000.2790.0410.110
ashneer_present0.8030.7590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0680.0000.0000.3110.3110.8610.1760.0001.0000.1720.6920.3660.0730.0000.2310.0590.3830.000
ashneer_invested0.2250.1550.0000.0960.0000.3570.2540.3410.1700.5430.2760.0000.3730.0000.0000.0000.1410.2650.1590.1721.0000.0560.0000.0000.0000.0000.1650.0740.150
ghazal_present0.7940.7710.0000.1190.0000.0000.0000.0000.0000.1610.0000.0000.0000.0000.0000.0000.7950.2040.0000.6920.0561.0000.4230.0940.0000.2550.0000.4180.082
ghazal_invested0.3490.3610.0000.0000.0000.0000.2390.0000.0000.3950.0000.0000.1590.0000.0000.0000.4200.0350.0380.3660.0000.4231.0000.0000.1640.0350.1290.1510.364
namita_present0.5810.7330.0000.0000.0000.0000.1950.0000.1190.0000.0000.2210.0000.0000.0000.0000.0330.0000.0000.0730.0000.0940.0001.0000.0800.1120.0000.2240.000
namita_invested0.0000.0000.0000.0000.0000.4270.2650.2830.0000.5470.3380.0000.4100.0000.0000.0000.0000.2650.0000.0000.0000.0000.1640.0801.0000.0000.1130.0410.110
peyush_present0.8410.7580.0000.2490.1020.1140.0000.1640.0000.0620.0000.0000.0000.0000.0000.0000.1960.0000.0000.2310.0000.2550.0350.1120.0001.0000.2710.4520.122
peyush_invested0.2480.2060.0000.0000.0000.4820.3550.2110.3080.5630.2960.1830.4580.1560.0000.0000.0670.0000.2790.0590.1650.0000.1290.0000.1130.2711.0000.2080.000
vineeta_present0.7380.7960.0000.1440.0680.0570.0000.0000.0000.0000.0000.0000.0920.0000.0630.0630.3350.1860.0410.3830.0740.4180.1510.2240.0410.4520.2081.0000.296
vineeta_invested0.2370.3130.0000.0000.0000.4140.4500.0000.0000.4460.4430.2130.3080.0000.0000.0000.0000.0000.1100.0000.1500.0820.3640.0000.1100.1220.0000.2961.000

Missing values

2023-05-28T12:18:55.916582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-28T12:18:56.461093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

episode_numberstartup_numberbrand_namedescriptiondeal_offeredstartup_ask_amount_lakhsstartup_ask_percentagestartup_ask_valuationdeal_amount_lakhsdeal_equitydeal_valuationloan_element_presentloan_amountrannvijay_presentabish_presentaman_presentaman_investedanupam_presentanupam_investedashneer_presentashneer_investedghazal_presentghazal_investednamita_presentnamita_investedpeyush_presentpeyush_investedvineeta_presentvineeta_investedsharks_offeringamount_per_sharkequity_per_shark
011BluePine IndustriesFrozen Momos150.05.001000.0075.016.00468.75001011101100100011325.05.333333
112Booz scootersRenting e-bike for mobility in private spaces140.015.00266.6740.050.0080.00001010101100100011220.025.000000
213Heart up my SleevesDetachable Sleeves125.010.00250.0025.030.0083.33001010111000100011212.515.000000
324Tagz FoodsHealthy Potato Chips170.01.007000.0070.02.752545.45001010101100100010170.02.750000
425Head and HeartBrain Development Course050.05.001000.000.00.000.0000101010100010001000.00.000000
526Agro tourismTourism050.05.001000.000.00.000.0000101010100010001000.00.000000
637Qzense LabsFood Freshness Detector0100.00.2540000.000.00.000.0000101010100010001000.00.000000
738PeeschuteDisposable Urine Bag175.04.001875.0075.06.001250.00001011101000100010175.06.000000
839NOCDEnergy Drink150.02.002500.0020.015.00133.331301010101000100011120.015.000000
9410CosiqIntelligent Skincare150.07.50666.6750.025.00200.00001010111000100011225.012.500000
episode_numberstartup_numberbrand_namedescriptiondeal_offeredstartup_ask_amount_lakhsstartup_ask_percentagestartup_ask_valuationdeal_amount_lakhsdeal_equitydeal_valuationloan_element_presentloan_amountrannvijay_presentabish_presentaman_presentaman_investedanupam_presentanupam_investedashneer_presentashneer_investedghazal_presentghazal_investednamita_presentnamita_investedpeyush_presentpeyush_investedvineeta_presentvineeta_investedsharks_offeringamount_per_sharkequity_per_shark
11134112Twee in OneReversible and convertible clothing030.07.50400.00.00.00.0000101010101010101000.00.0
11234113Green ProteinPlant-Based Protein060.02.003000.00.00.00.0000101010101010101000.00.0
11334114On2CookFastest Cooking Device0100.01.0010000.00.00.00.0000101010101010101000.00.0
11435115Jain ShikanjiLemonade140.08.00500.040.030.0133.33001011111110101011410.07.5
11535116WolooWashroom Finder050.04.001250.00.00.00.0000101010101010101000.00.0
11635117Elcare IndiaCarenting for Elders0100.02.504000.00.00.00.0000101010101010101000.00.0
11736118SneakareShoe care and storage solutions120.05.00400.021.012.0175.0000011110000011101137.04.0
11836119French CrownClothing Industry0150.00.3345000.00.00.00.0000011010000010101000.00.0
11936120Store My GoodsStorage solutions1100.01.755714.0100.04.02500.001500110100000111110250.02.0
12036121DevnagriTranslation platform0100.01.0010000.00.00.00.0000011010000010101000.00.0